Elefelious Getachew Belay

The rapid advancement of technology has been matched by a significant rise in cybercrime, posing substantial challenges for digital forensics investigators who must handle increasingly complex cases and navigate vast volumes of evidence. While current research on intent recognition has largely focused on cybersecurity measures for preventing attacks, there has been a noticeable gap in the integration of legal intent analysis with technical digital forensics. This paper addresses this gap by presenting an innovative model that combines legal and technical perspectives through a formal model. The model consists of three core components—Evidence Analysis, Intent Recognition, and a Criminal Repository—that systematically process digital evidence, reconstruct crime scenes, identify criminal intent, and offer recommendations for the conviction process. Using formal methods, the model rigorously defines key concepts such as crime, intent, and intent types, ensuring its robustness and reliability. By stimulating the model using phishing attack scenarios, we validate the model’s capability, demonstrating its ability to identify various types of intent and manage complex cases. Looking forward, we suggest implementing the model by incorporating advanced AI approaches, particularly Agentic AI, or combining logic-based methods with explainable AI. This advancement would help address huge volume-related challenges of digital forensics and provide a powerful tool for modern investigative practices.

Word Sense Disambiguation is determining the correct sense of an ambiguous word within context. It plays a crucial role in natural language applications such as machine translation, question-answering, chatbots, information retrieval, sentiment analysis, and overall language comprehension. Recent advancements in this area have focused on utilizing deep contextual models to address these challenges. However, despite this positive progress, semantical and syntactical ambiguity remains a challenge, especially when dealing with polysomy words, and it is considered an AI-complete problem. In this work, we propose an approach that integrates hierarchical attention mechanisms and BERT embeddings to enhance WSD performance. Our model, incorporating local and global attention, demonstrates significant improvements in accuracy, particularly in complex sentence structures. To the best of our knowledge, our model is the first to incorporate hierarchical attention mechanisms integrated with contextual embedding. We conducted experiment on publicly available datasets for English and Italian language. Experimental results show that our model achieves state-of-the-art results in WSD, surpassing baseline models up to 2.9% F1 accuracy on English WSD. Additionally, it demonstrates superior performance in Italian WSD, outperforming existing papers up to 0.7% F1 accuracy. We further adapted the model for Amharic word sense disambiguation. Despite the absence of a standard benchmark dataset for Amharic WSD, our model achieved an accuracy of 92.4% on a dataset we prepared ourselves. Our findings underscore the significance of linguistic features in contextual information capture for WSD. While Part-of-Speech (POS) tagging has a limited impact, word embeddings significantly influence performance. Local and global attention further improve results, particularly at the word level. Overall the results emphasize the importance of context in WSD, advancing context-aware natural language processing systems.

In the literature, there are polarized views regarding the capabilities of technology to embed societal values. One aisle of the debate contends that technical artifacts are value-neutral since values are not peculiar to inanimate objects. Scholars on the other side of the aisle argue that technologies tend to be value-laden. With the call to embed ethical values in technology, this article explores how AI and other adjacent technologies are designed and developed to foster social justice. Drawing insights from prior studies, this paper identifies seven African moral values considered central to actualizing social justice; of these, two stand out—respect for diversity and ethnic neutrality. By introducing use case analysis along with the Discovery, Translation, and Verification (DTV) framework and validating via Focus Group Discussion, this study revealed novel findings: first, ethical value analysis is best carried out alongside software system analysis. Second, to embed ethics in technology, interdisciplinary expertise is required. Third, the DTV approach combined with the software engineering methodology provides a promising way to embed moral values in technology. Against this backdrop, the two highlighted ethical values—respect for diversity and ethnic neutrality—help ground the pursuit of social justice.

Cloud manufacturing allows multiple manufacturers to contribute their manufacturing facilities and assets for monitoring, operating, and controlling common processes of manufacturing and services controlled through cloud computing. The modern framework is driven by the seamless integration of technologies evolved under Industry 4.0. The entire digitalized manufacturing systems operate through the Internet, and hence, cybersecurity threats have become a problem area for manufacturing companies. The impacts can be very serious because cyber-attacks can penetrate operations carried out in the physical infrastructure, causing explosions, crashes, collisions, and other incidents. This research is a thematic literature review of the deterrence to such attacks by protecting IoT devices by employing provenance blockchain and artificial intelligence. The literature review was conducted on four themes: cloud manufacturing design, cybersecurity risks to the IoT, provenance blockchains for IoT security, and artificial intelligence for IoT security. These four themes of the literature review were critically analyzed to visualize a framework in which provenance blockchain and artificial intelligence can be integrated to offer a more effective solution for protecting IoT devices used in cloud manufacturing from cybersecurity threats. The findings of this study can provide an informative framework.

Floorplan synthesis is the process of generating new, realistic floor plans for buildings and homes using machine learning and generative models. In recent years, various generative methods, including GANs and diffusion models, have been utilized for the task of floorplan generation, demonstrating promising advancements in architectural design and planning. However, despite their potential, these methods face unique challenges like mode collapse, training instability, and sampling time, which require innovative solutions to overcome for further progress in this field. To address these issues, various techniques such as regualrization techniques, architectural modifications, and optimization algorithms, have been employed. However, existing techniques still struggle to balance both sampling time and diversity simultaneously. In response, HouseGanDi proposes a novel hybrid approach that amalgamates GANs and diffusion models to address the dual challenges of diversity and sampling time in floorplan generation. To the best of our knowledge, this work is the first to introduce a solution that not only balances sampling time and diversity but also enhances the realism of the generated floorplans. HouseGanDi is trained on the RPLAN dataset and combines the advantages of GANs and diffusion models in multimodal fashion while incorporating different techniques such as regularization methods and architectural modifications to optimize our objectives. The multimodality allows our model to jump a number of denoising steps while capturing data distributions. To evaluate the effect of the denoising step, we experimented with different time steps and found better diversity results at T=20. Evaluation of diversity using FID demonstrates an average 15.5% improvement over the state-of-the-art houseDiffusion model, with a 41% reduction in generation time. However, challenges persist in generating non-orthogonal floorplans and accommodating intricate spatial layouts.

In this systematic literature review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a PRISMA systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, artificial intelligence black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses a challenge in the digital forensics landscape. However, it is imperative that models developed for digital forensics possess intrinsic attributes of explainability and logical coherence, thereby fostering judicial confidence, mitigating biases, and upholding accountability for their determinations. To this end, we advocate for hybrid solutions that blend explainability, reasonableness, efficiency, and accuracy. Furthermore, we propose the creation of a taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.